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https://github.com/PaddlePaddle/FastDeploy.git
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[Feature] Unify the registration name recognition for tool_parser and reasoning_parser to “-” (#4668)
* parser register name unify * change ernie_x1 to ernie-x1 * change ernie4_5_vl to ernie-45-vl * fix unit test
This commit is contained in:
@@ -1,5 +1,5 @@
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reasoning-parser: ernie_x1
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tool_call_parser: ernie_x1
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reasoning-parser: ernie-x1
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tool_call_parser: ernie-x1
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tensor_parallel_size: 4
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max_model_len: 65536
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max_num_seqs: 128
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@@ -1,7 +1,7 @@
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tensor_parallel_size: 1
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max_model_len: 131072
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max_num_seqs: 32
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reasoning_parser: ernie_x1
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tool_call_parser: ernie_x1
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reasoning_parser: ernie-x1
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tool_call_parser: ernie-x1
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load_choices: "default_v1"
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quantization: wint8
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@@ -33,8 +33,8 @@ python -m fastdeploy.entrypoints.openai.api_server \
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--tensor-parallel-size 1 \
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--max-model-len 131072 \
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--quantization wint8 \
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--reasoning-parser ernie_x1 \
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--tool-call-parser ernie_x1 \
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--reasoning-parser ernie-x1 \
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--tool-call-parser ernie-x1 \
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--max-num-seqs 32
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```
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- `--quantization`: Indicates the quantization strategy used by the model. Different quantization strategies will result in different performance and accuracy of the model. It could be one of `wint8` / `wint4` / `block_wise_fp8`(Hopper is needed).
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@@ -80,7 +80,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
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# Whether to use Machete for wint4 dense GEMM.
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"FD_USE_MACHETE": lambda: os.getenv("FD_USE_MACHETE", "1"),
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# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie4_5_vl, \n</think>\n\n for ernie_x1)
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# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-vl, \n</think>\n\n for ernie-x1)
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"FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),
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# Timeout for cache_transfer_manager process exit
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@@ -33,8 +33,8 @@ python -m fastdeploy.entrypoints.openai.api_server \
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--tensor-parallel-size 1 \
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--max-model-len 131072 \
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--quantization wint8 \
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--reasoning-parser ernie_x1 \
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--tool-call-parser ernie_x1 \
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--reasoning-parser ernie-x1 \
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--tool-call-parser ernie-x1 \
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--max-num-seqs 32
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```
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其中:
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@@ -80,7 +80,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
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# 是否使用 Machete 后端的 wint4 GEMM.
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"FD_USE_MACHETE": lambda: os.getenv("FD_USE_MACHETE", "1"),
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# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie4_5_vl, \n</think>\n\n for ernie_x1)
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# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-vl, \n</think>\n\n for ernie-x1)
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"FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),
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# cache_transfer_manager 进程残留时退出等待超时时间
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@@ -95,6 +95,7 @@ class ToolParserManager:
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Raise a KeyError exception if the name is not registered.
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"""
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name = name.replace("_", "-")
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if name in cls.tool_parsers:
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return cls.tool_parsers[name]
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@@ -44,7 +44,7 @@ from fastdeploy.entrypoints.openai.tool_parsers.abstract_tool_parser import (
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from fastdeploy.utils import data_processor_logger
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@ToolParserManager.register_module("ernie_45-vl-thinking")
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@ToolParserManager.register_module("ernie-45-vl-thinking")
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class Ernie45VLThinkingToolParser(ToolParser):
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"""
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Tool parser for Ernie model version 4.5.1.
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@@ -44,7 +44,7 @@ from fastdeploy.entrypoints.openai.tool_parsers.abstract_tool_parser import (
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from fastdeploy.utils import data_processor_logger
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@ToolParserManager.register_module("ernie_x1")
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@ToolParserManager.register_module("ernie-x1")
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class ErnieX1ToolParser(ToolParser):
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"""
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Tool parser for Ernie model version 4.5.1.
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@@ -122,7 +122,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
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"FD_ENABLE_SWAP_SPACE_CLEARING": lambda: int(os.getenv("FD_ENABLE_SWAP_SPACE_CLEARING", "0")),
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# enable return text, used when FD_ENABLE_INTERNAL_ADAPTER=1
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"FD_ENABLE_RETURN_TEXT": lambda: bool(int(os.getenv("FD_ENABLE_RETURN_TEXT", "0"))),
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# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie4_5_vl, \n</think>\n\n for ernie_x1)
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# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-vl, \n</think>\n\n for ernie-x1)
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"FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),
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# Timeout for cache_transfer_manager process exit
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"FD_CACHE_PROC_EXIT_TIMEOUT": lambda: int(os.getenv("FD_CACHE_PROC_EXIT_TIMEOUT", "600")),
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@@ -101,7 +101,7 @@ def limit_thinking_content_length(
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line_break_id: int = None,
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):
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if limit_strategy == "</think>":
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# for ernie4_5_vl
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# for ernie-45-vl
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limit_thinking_content_length_v1(
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sampled_token_ids,
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max_think_lens,
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@@ -110,7 +110,7 @@ def limit_thinking_content_length(
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think_end_id,
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)
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elif limit_strategy == "\n</think>\n\n":
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# for ernie_x1
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# for ernie-x1
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assert line_break_id > 0
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limit_thinking_content_length_v2(
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sampled_token_ids,
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@@ -136,7 +136,7 @@ def speculate_limit_thinking_content_length(
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line_break_id: int = None,
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):
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if limit_strategy == "</think>":
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# for ernie4_5_vl
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# for ernie-45-vl
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speculate_limit_thinking_content_length_v1(
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accept_tokens,
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max_think_lens,
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@@ -147,7 +147,7 @@ def speculate_limit_thinking_content_length(
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think_end_id,
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)
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elif limit_strategy == "\n</think>\n\n":
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# for ernie_x1
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# for ernie-x1
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assert line_break_id > 0
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speculate_limit_thinking_content_length_v2(
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accept_tokens,
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@@ -125,6 +125,7 @@ class ReasoningParserManager:
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Raise a KeyError exception if the name is not registered.
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"""
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name = name.replace("_", "-")
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if name in cls.reasoning_parsers:
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return cls.reasoning_parsers[name]
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@@ -5,10 +5,10 @@ from fastdeploy.entrypoints.openai.protocol import ChatCompletionRequest, DeltaM
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from fastdeploy.reasoning import ReasoningParser, ReasoningParserManager
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@ReasoningParserManager.register_module("ernie_x1")
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@ReasoningParserManager.register_module("ernie-x1")
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class ErnieX1ReasoningParser(ReasoningParser):
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"""
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Reasoning parser for ernie_x1 model with stricter boundary checking.
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Reasoning parser for ernie-x1 model with stricter boundary checking.
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Unified rules:
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- Do not strip newline before </think>
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@@ -203,7 +203,7 @@ def xpu_post_process(
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step_idx = share_inputs["step_idx"]
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limit_think_status = share_inputs["limit_think_status"]
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if limit_strategy == "</think>":
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# for ernie4_5_vl
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# for ernie-45-vl
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limit_thinking_content_length_v1(
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sampled_token_ids,
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max_think_lens,
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@@ -212,7 +212,7 @@ def xpu_post_process(
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think_end_id,
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)
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elif limit_strategy == "\n</think>\n\n":
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# for ernie_x1
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# for ernie-x1
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assert line_break_id > 0
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limit_thinking_content_length_v2(
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sampled_token_ids,
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@@ -73,9 +73,9 @@ class TestOpenAIServingCompletion(unittest.TestCase):
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self.assertTrue(serving_completion._check_master())
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def test_calc_finish_reason_tool_calls(self):
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# 创建一个模拟的engine_client,并设置reasoning_parser为"ernie_x1"
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# 创建一个模拟的engine_client,并设置reasoning_parser为"ernie-x1"
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engine_client = Mock()
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engine_client.reasoning_parser = "ernie_x1"
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engine_client.reasoning_parser = "ernie-x1"
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# 创建一个OpenAIServingCompletion实例
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "ips", 360)
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# 创建一个模拟的output,并设置finish_reason为"tool_call"
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@@ -86,9 +86,9 @@ class TestOpenAIServingCompletion(unittest.TestCase):
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assert result == "tool_calls"
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def test_calc_finish_reason_stop(self):
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# 创建一个模拟的engine_client,并设置reasoning_parser为"ernie_x1"
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# 创建一个模拟的engine_client,并设置reasoning_parser为"ernie-x1"
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engine_client = Mock()
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engine_client.reasoning_parser = "ernie_x1"
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engine_client.reasoning_parser = "ernie-x1"
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# 创建一个OpenAIServingCompletion实例
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serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "ips", 360)
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# 创建一个模拟的output,并设置finish_reason为其他值
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@@ -91,7 +91,7 @@ class TestReasoningParserManager(unittest.TestCase):
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Test that a parser can be registered and retrieved successfully.
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Verifies normal registration and retrieval functionality.
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"""
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ReasoningParserManager.register_module(module=TestReasoningParser, name="test_parser", force=True)
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ReasoningParserManager.register_module(module=TestReasoningParser, name="test-parser", force=True)
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parser_cls = ReasoningParserManager.get_reasoning_parser("test_parser")
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self.assertIs(parser_cls, TestReasoningParser)
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